{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a3769cd-bd0e-43dc-afbe-6fdf60553d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a= [1702.22424242] b= [-922.33333333]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "x=np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]])\n",
    "y=np.array([[2000],[2200],[4900],[3221],[6834],[10567],[9566],[15678],[13644],[15789]])\n",
    "model=LinearRegression()\n",
    "model.fit(x,y)\n",
    "print(\"a=\",model.coef_[0],\"b=\",model.intercept_)\n",
    "y2=model.predict(x)\n",
    "plt.xlabel('面积')\n",
    "plt.ylabel('售价')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([0,20,1500,20000])\n",
    "plt.scatter(x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,y2,'r-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d62a704-e187-4c42-87b7-3b6b305e52ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
